CN101984437A - Music resource individual recommendation method and system thereof - Google Patents
Music resource individual recommendation method and system thereof Download PDFInfo
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Abstract
The invention discloses a music resource individual recommendation method and a system thereof. The method includes that: text information related to music resource is captured from network; statistics on label seed in related text information of each music resource and frequency of each label seed and statistics on label seed in related text information of all the music resource and frequency of each label seed are carried out, thus obtaining global weight of each label seed; for each single music resource, monomer weight of each label seed corresponding to single music resource is obtained according to the frequency of each label seed and global weight thereof; label seeds monomer weight of which are consistent with preset conditions are determined to be a label of music resource; and individual recommendation is carried out according to the label of music resource. By adopting the invention, automatic and relatively accurate individual recommendation can be realized.
Description
Technical field
The present invention relates to the recommendation of personalized information technical field, particularly relate to music sources personalized recommendation method and system.
Background technology
Along with Internet development, music sources has been called very large ingredient in the internet, applications.Each big music site, music software etc. provide millions of music sources for the user.But the user but can't understand all at short notice, therefore also is difficult to find easily the own music of being liked.This just need be by coming for the user recommends the interested music of its possibility intelligently also promptly personalized music recommend someway.
The method of current personalized recommendation mainly is collaborative filtering recommending algorithm (CollaborativeFiltering Recommendation).It is by collection user's music behavioural habits, and the user calculates the music list of each user's preferences to the evaluation of music; And then find out the similar customer group of interest, and the music that this customer group is paid close attention to is carried out analysis-by-synthesis, the unique user in this group, the music of other user's preferences in this group is exactly the music that this user may like.As shown in Figure 1, the vertical rectangle song of listening of representative of consumer A, B, C, D is respectively tabulated, collaborative filtering is at first found out user A, B, C, the D of part same music hobby, and with user B, C, D total and music recommend that user A does not have is given user A.
But this collaborative filtering recommending algorithm has following shortcoming at least, and the eternal recommendation of being paid close attention to by Any user of concert is not never come out.For this reason, prior art also provides the information filtering proposed algorithm, this method need add label (tag) for each music sources, because the label of music sources has been represented information such as the described classification of music sources, for example jazz, rock and roll or the like, therefore, can carry out personalized recommendation according to the label of music sources and user's hobby.
Yet prior art needs a large amount of professionals to come the label of music is extracted, marks, and workload is very big, and efficient is low; And annotation results is subjected to the influence of factor and individual subjective factor to a certain extent, may be inaccurate.
Summary of the invention
The invention provides music sources personalized recommendation method and system, can realize robotization and personalized recommendation relatively accurately.
The invention provides following scheme:
A kind of music sources personalized recommendation method comprises:
Grasp the text message relevant from network with music sources;
Described text message is cut speech, add up the number of times that the label seed that occurs in the relevant textual information of each music sources and each label seed occur, and the number of times that occurs of the label seed that occurs in the relevant textual information of all music sources and each label seed;
The number of times that the label seed that occurs in the relevant textual information according to described all music sources and each label seed occur obtains the overall weight of each label seed;
For single music sources, number of times and overall weight thereof according to each label seed in its relevant textual information occurs obtain the monomer weight of each label seed with respect to single music sources;
The label seed that the monomer weight is met prerequisite is defined as the label of music sources;
Label according to described music sources carries out personalized recommendation.
Preferably, described label according to described music sources carries out personalized recommendation and comprises:
For music sources set, merge the label of each music sources in this set;
According to each label with respect to the monomer weight in each music sources, the weight of each label after obtaining merging;
The label that weight is met prerequisite is defined as the label that this music sources is gathered;
Label according to described music sources set carries out personalized recommendation.
Preferably, described music sources set comprises each singer's music sources, and the label of described music sources set comprises each singer's label, and described label according to described music sources carries out personalized recommendation and comprises:
The weight of each singer's label is mapped to vector space, obtains each singer's label vector;
Cosine angle between the computation tag vector obtains each singer similarity between any two in twos;
Carry out personalized recommendation according to the similarity between the singer.
Preferably, described music sources set comprises all music sources that each user listened, and the label of described music sources set comprises each user's label, and described label according to described music sources carries out personalized recommendation and comprises:
Label based on each music sources is set up inverted list, obtains the music sources tabulation of each label correspondence;
Carry out personalized recommendation according to user's the label and the music sources tabulation of label correspondence.
Preferably, also comprise:
According to each user's the behavior of listening to historical record, adjust each label weight of each user relatively.
Preferably, also comprise:
According to weight rank order from big to small, and will come the label of the preset number of front to user's label, be defined as this user's popular label;
The described label and the music sources tabulation of label correspondence according to the user carried out personalized recommendation and comprised: carry out personalized recommendation according to described user's the popular label and the music sources tabulation of label correspondence.
Preferably, also comprise:
According to the weight of each user's label, calculate the average weight of each label in all users;
This user's the label size according to the merchant of this user's relatively weight and described average weight is sorted, and will come the label of the preset number of front, be defined as this user's long-tail label;
The described label and the music sources tabulation of label correspondence according to the user carried out personalized recommendation and comprised: carry out personalized recommendation according to described user's the long-tail label and the music sources tabulation of label correspondence.
A kind of music sources personalized recommendation system comprises:
The information placement unit is used for grasping the text message relevant with music sources from network;
Statistic unit, be used for described text message is cut speech, add up the number of times that the label seed that occurs in the relevant textual information of each music sources and each label seed occur, and the number of times that occurs of the label seed that occurs in the relevant textual information of all music sources and each label seed;
Overall situation weight acquiring unit is used for the number of times that label seed that the relevant textual information according to described all music sources occurs and each label seed occur, and obtains the overall weight of each label seed;
Monomer weight acquiring unit is used for for single music sources, and number of times and overall weight thereof according to each label seed in its relevant textual information occurs obtain the monomer weight of each label seed with respect to single music sources;
The music label determining unit is used for the label seed that the monomer weight meets prerequisite is defined as the label of music sources;
Recommendation unit is used for carrying out personalized recommendation according to the label of described music sources.
Preferably, described recommendation unit comprises:
Label merges subelement, is used for merging the label of each music sources in this set for music sources set;
Weight merges subelement, is used for according to the monomer weight of each label with respect to each music sources, the weight of each label after obtaining merging;
The set label is determined subelement, is used for the label that weight meets prerequisite is defined as the label of this music sources set;
First recommends subelement, is used for carrying out personalized recommendation according to the label of described music sources set.
Preferably, described music sources set comprises each singer's music sources, and the label of described music sources set comprises each singer's label, and described first recommends subelement to comprise:
The label vector obtains subelement, is used for the weight of each singer's label is mapped to vector space, obtains each singer's label vector;
The similarity computation subunit is used for the cosine angle between the computation tag vector in twos, obtains each singer similarity between any two;
The singer recommends subelement, is used for carrying out personalized recommendation according to the similarity between the singer.
Preferably, described music sources set comprises all music sources that each user listened, and the label of described music sources set comprises each user's label, and described first recommends subelement to comprise:
Arrange subelement, be used for setting up inverted list, obtain the music sources tabulation of each label correspondence based on the label of each music sources;
Label is recommended subelement, is used for carrying out personalized recommendation according to user's the label and the music sources tabulation of label correspondence.
Preferably, also comprise:
Weight adjustment unit is used for the behavior of the listening to historical record according to each user, adjusts each label weight of each user relatively.
Preferably, also comprise:
Popular label acquiring unit is used for label to the user according to weight rank order from big to small, and will come the label of the preset number of front, is defined as this user's popular label;
Described label recommends subelement specifically to be used for: carry out personalized recommendation according to described user's the popular label and the music sources tabulation of label correspondence.
Preferably, also comprise:
The average weight computing unit is used for the weight according to each user's label, calculates the average weight of each label in all users;
Long-tail label acquiring unit is used for this user's the label size according to the merchant of this user's relatively weight and described average weight is sorted, and will comes the label of the preset number of front, is defined as this user's long-tail label;
Described label recommends subelement specifically to be used for: carry out personalized recommendation according to described user's the long-tail label and the music sources tabulation of label correspondence.
According to specific embodiment provided by the invention, the invention discloses following technique effect:
The present invention is by grasping the relevant textual information of music sources automatically from network, automatically from relevant textual information, extract the label of music sources then, so that the label according to music sources carries out personalized music sources recommendation to the user, therefore, can realize robotization and personalized recommendation relatively accurately.
Description of drawings
In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art, to do to introduce simply to the accompanying drawing of required use among the embodiment below, apparently, accompanying drawing in describing below only is some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is a recommend method synoptic diagram of the prior art;
Fig. 2 is the process flow diagram of the method that provides of the embodiment of the invention;
Fig. 3 is the synoptic diagram of the system that provides of the embodiment of the invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the invention, the technical scheme in the embodiment of the invention is clearly and completely described, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, the every other embodiment that those of ordinary skills obtained belongs to the scope of protection of the invention.
Referring to Fig. 2, the embodiment of the invention provides a kind of music sources personalized recommendation method, and this method may further comprise the steps:
S201: grasp the text message relevant with music sources from network;
Wherein, the operation of grasping can be finished by the program of robotization, wherein the text message that the music sources of Zhua Quing is relevant can comprise the label (tag) that website or online friend mark music, the described classified information of music, associated album name, list title or the like.
S202: described text message is cut speech, add up the number of times that the label seed that occurs in the relevant textual information of each music sources and each label seed occur, and the number of times that occurs of the label seed that occurs in the relevant textual information of all music sources and each label seed;
Because the text message that grasps from network may be a sentence, also may be article paragraph or the like, therefore, also need the text message that grasps is cut speech.Also promptly, with sentence or article paragraph according in be syncopated as word one by one according to grammatical and semantic etc.Certainly, the process of cutting is also finished automatically, can adopt the method that provides in the prior art when specifically carrying out automatic segmentation, repeats no more here.
Statistics for label, because the label of music sources is used to identify the feature that music sources has, therefore, during specific implementation, can set in advance some label seeds, for example " popular ", " rock and roll ", " popular " or the like are determined the label of each music sources by these label seeds.For this reason, also need be after the relevant textual information of certain music sources be carried out cutting, at first count the number of times that occurs each label seed in the word that these cuttings obtain, " popular " 5 times for example may appear in the relevant textual information of certain music sources, " rock and roll " 8 times, " popular " 0 time, or the like.In addition, the number of times of each label seed appears, for example in the music sources relevant textual information that also needs to add up all, total music sources 100 head in the relevant textual information of these music sources, " popular " may occur 500 times, " rock and roll " 200 times, " popular " 300 times, or the like.
S203: the number of times that the label seed that occurs in the relevant textual information according to described all music sources and each label seed occur obtains the overall weight of each label seed;
During specific implementation, can each music sources as a file, be adopted the overall weight (for example, can be designated as W_tag_global) of calculating each label seed based on the method for TF-IDF with the label seed as term.Wherein, TF-IDF is a kind of statistical method, in order to assess the significance level of a words for a copy of it file in a file set or the corpus.The number of times that the importance of words occurs hereof along with it increase that is directly proportional, but the decline that can be inversely proportional to along with the frequency that it occurs in file set or corpus simultaneously.
For a label seed, overall weight is high more, represents that then this label seed is representative more, and discrimination is also just high more.By this method, not only the label seed of some entanglements is filtered owing to occurrence number is very few, and some occurrence numbers are a lot of but do not have the label seed of discrimination (for example " Chinese ", " popular " etc.) also can be filtered.
S204: for single music sources, number of times and overall weight thereof according to each label seed in its relevant textual information occurs obtain the monomer weight of each label seed with respect to single music sources;
The monomer weight can be represented by W_tag_single.
S205: the label seed that the monomer weight is met prerequisite is defined as the label of music sources;
For example, for a music sources, the label seed that the monomer weight can be ranked in the top is as the label of this music sources.
S206: the label according to described music sources carries out personalized recommendation.
Realized obtaining automatically of music sources label by aforementioned several steps, like this, just can carry out the personalized recommendation of music sources according to the label of each music sources.
Specifically when the label that utilizes music sources carries out personalized recommendation, multiple implementation can be arranged, at length introduce respectively below.
In the practical application, can have some music sources set usually, for example, music list, singer's special edition or the like are for these music sources set.Can calculate the label of music sources set, and carry out the personalized recommendation of music sources according to the label of collection of music.During the label of concrete computational music resource collection, can at first merge the label of each music sources in this set, according to each label with respect to the monomer weight in each music sources and the significance level of each music sources, the weight of each label after obtaining merging; The label that weight is met prerequisite is defined as the label that this music sources is gathered.
For example, comprise music sources a, b, c in certain music sources set A, have three labels 1,2,3 respectively, wherein for music sources a, the weight of each label is respectively (t
A1, t
A2, t
A3), for music sources b, the weight of each label is respectively (t
B1, t
B2, t
B3), for music sources c, the weight of each label is respectively (t
C1, t
C2, t
C3), then after the label merging with each music sources, the weight of each label that obtains is (t
A1+ t
B1+ t
C1, t
A2+ t
B2+ t
C2, t
A3+ t
B3+ t
C3).Then, the weight after just each can being merged is arranged according to order from big to small, and the several weights as this music sources set that come the front are got final product.
Need to prove, because each music sources during a music sources is gathered may have different labels, therefore, when the label to each music sources merges, it is the equal of the union of getting the list of labels of each music sources, simultaneously, the monomer weight addition of same label in each music sources.For example, suppose to comprise music sources a, b, c in certain music sources set A that wherein, music sources a has three labels 1,2,3, and for this music sources a, the weight of each label is respectively (t
A1, t
A2, t
A3); Music sources b has two labels 1,2, and for this music sources b, the weight of each label is respectively (t
B1, t
B2); Music sources c has three labels 2,3,4, and for this music sources c, the weight of each label is respectively (t
C2, t
C3, t
C4), then after the label merging with each music sources, the weight of each label that obtains is (t
A1+ t
B1, t
A2+ t
B2+ t
C2, t
A3+ t
C3, t
C4).
Certainly, when extracting the label of music sources set, can also calculate in conjunction with the significance level of each music sources.Also promptly, when the label of computational music resource collection, important music sources, the weight of its label has higher weighted value.Wherein, the significance level of music sources can adopt its popular degree to calculate.For example, suppose to comprise music sources a, b in certain music sources set A that wherein, music sources a has two labels 1,2, and for this music sources a, the weight of each label is respectively (t
A1, t
A2); Music sources b has two labels 1,2, and for this music sources b, the weight of each label is respectively (t
B1, t
B2); If the importance degree of music sources a is higher than music sources b, then after the label merging with each music sources, the weight of each label that obtains can be (x* (t
A1+ t
B1), y* (t
A2+ t
B2)), wherein, 0＜y＜x＜1, x+y=1.
In actual applications, the user may submit a plurality of music sources in modes such as music lists, at this moment, can calculate the label of this music list according to preceding method, then according to the label of this list, and the label of the music sources that calculates set, recommend other music sources set to the user.
Perhaps, if each music sources of a singer is formed a music sources set, the label of then aforementioned music sources set just can be represented each singer's label.Also promptly, the label of a plurality of music sources of a singer is merged, finally can obtain this singer's label, in order to the characteristics of representing this singer to have.On this basis, can also calculate the similarity between each singer,, carry out the personalized recommendation of music sources to the user according to singer's similarity.Concrete, the weight of each singer's label can be mapped to vector space, obtain each singer's label vector, by the mode of the cosine angle between the computation tag vector in twos, just can obtain each singer similarity between any two then.When carrying out personalized recommendation according to the similarity between the singer, can be after the user submit certain singer to, this singer and other singers' similarity is sorted, the singer that rank is forward recommends this user as this singer's similar singer.
Certainly, when calculating the similarity between the singer according to the method described above, the label vector that only has identical vector space just has comparability, also be, suppose that certain singer a, b have three labels 1,2,3, wherein for music sources a, the weight of each label is respectively (t
A1, t
A2, t
A3), for music sources b, the weight of each label is respectively (t
B1, t
B2, t
B3), then can calculate this two singers' similarity by following formula:
Otherwise, if the vector space difference of two singers' label vector then obviously can't utilize the mode of cosine angle to calculate both similarities.But, in this case, can calculate two singers' similarity according to the mode of cosine angle, directly two singers are defined as uncorrelated getting final product.For example, singer a has label 1,2,3, and singer b has label 1,4,5, shows obviously that then these two singers' similarity is lower.
Except a singer's music sources is gathered as a music sources, the music sources that a user can also be listened is formed a music sources set, like this, by obtaining the label of music sources set, can obtain this user's label, in order to the interest of listening to of representative of consumer, like this, can also carry out the personalized recommendation of music sources to the user according to user's label.During specific implementation, can obtain user's the daily record of listening to, history is sung in listening of recording user, and the music sources that so just a user can be listened is formed a music sources set, and extracting the label of music sources set according to preceding method, this label just can be used as this user's label.Certainly, other users also can do similar processing.
When specifically carrying out personalized recommendation, except obtaining user's label, can also set up inverted list based on the label of all music sources, obtain the music sources tabulation of each label correspondence, like this, according to user's label, and the music sources of label correspondence, just can carry out personalized recommendation to the user.During specific implementation, after the label that has obtained each user, can in system, preserve the corresponding relation between user ID and its label, when the user uses the ID of oneself to sign in in the system, just can obtain the label of this user ID correspondence, then the music sources of this label correspondence be recommended this user and get final product.
Need to prove, can also adjust each label weight of each user relatively according to user's the historical record of listening to.Wherein, listen to that historical record can comprise time of listening to, listen to the source (comprise active searching, click list, local disk etc.), user's behavior (comprising online playing or download or the like), whether repeat to listen to, to evaluation of music sources favorable rating or the like.
During specific implementation,, but might not each label can both well embody user's the interest of listening to because after the label of all music sources that a user is listened to merged, the user tag that obtains had a lot of.Therefore, can with to user's label according to weight rank order from big to small, and will come the label of the preset number of front, and be defined as this user's popular label, carry out personalized recommendation according to user's the popular label and the music sources tabulation of label correspondence then.Wherein, because the ordering of aforementioned user tag is to be decided by the music sources that user self listens to fully, it doesn't matter with music sources that other users listen to, therefore, several labels that come the front in this ordering is called this user's popular label.
Also may there be this situation in actual applications: some label of certain user, may in this user's list of labels, sort comparatively lean on after, but its weight wants high a lot of with respect to the weight of other users' same label, in fact this label can embody this user's special preferences, therefore, can be referred to as the long-tail label.During specific implementation, each user's the weight of label can add up, calculate the average weight (be designated as w_avg) of each label in all users, the weight of supposing each label of each user is w, then this user's the label value according to w/w_avg can be sorted, come the label of front by this sort method, be this user's long-tail label, the long-tail label has been described user's the popular special preferences that is different from.Just can carry out personalized recommendation to this user then according to user's the long-tail label and the music sources tabulation of label correspondence.
In a word, the embodiment of the invention can be obtained the label of music sources automatically, carries out the personalized recommendation of music sources then on this basis to the user, therefore, can realize robotization and personalized recommendation relatively accurately.
Corresponding with the music sources personalized recommendation method that the embodiment of the invention provides, the embodiment of the invention also provides a kind of music sources personalized recommendation system, and referring to Fig. 3, this system comprises:
Overall situation weight acquiring unit 303 is used for the number of times that label seed that the relevant textual information according to described all music sources occurs and each label seed occur, and obtains the overall weight of each label seed;
Monomer weight acquiring unit 304 is used for for single music sources, and number of times and overall weight thereof according to each label seed in its relevant textual information occurs obtain the monomer weight of each label seed with respect to single music sources;
Music label determining unit 305 is used for the label seed that the monomer weight meets prerequisite is defined as the label of music sources;
Wherein, recommendation unit 306 can comprise:
Label merges subelement, is used for merging the label of each music sources in this set for music sources set;
Weight merges subelement, is used for according to the monomer weight of each label with respect to each music sources, the weight of each label after obtaining merging;
The set label is determined subelement, is used for the label that weight meets prerequisite is defined as the label of this music sources set;
First recommends subelement, is used for carrying out personalized recommendation according to the label of described music sources set.
Wherein, described music sources set can be each singer's a music sources, and the label of the music sources set that gets access to just can be represented each singer's label, and is corresponding, can carry out similar singer's recommendation, and at this moment, first recommends subelement to comprise:
The label vector obtains subelement, is used for the weight of each singer's label is mapped to vector space, obtains each singer's label vector;
The similarity computation subunit is used for the cosine angle between the computation tag vector in twos, obtains each singer similarity between any two;
The singer recommends subelement, is used for carrying out personalized recommendation according to the similarity between the singer.
Perhaps, all music sources that the music sources set also can make each user listen, at this moment, the label of the music sources set that gets access to just can be represented each user's label, and corresponding, first recommends subelement to comprise:
Arrange subelement, be used for setting up inverted list, obtain the music sources tabulation of each label correspondence based on the label of each music sources;
Label is recommended subelement, is used for carrying out personalized recommendation according to user's the label and the music sources tabulation of label correspondence.
In actual applications, when the foundation user tag is recommended, can also sing history according to listening of user, user tag is adjusted, at this moment, this system can also comprise:
Weight adjustment unit is used for the behavior of the listening to historical record according to each user, adjusts each label weight of each user relatively.
In addition, this system can also comprise:
Popular label acquiring unit is used for label to the user according to weight rank order from big to small, and will come the label of the preset number of front, is defined as this user's popular label;
Described label recommends subelement specifically to be used for: carry out personalized recommendation according to described user's the popular label and the music sources tabulation of label correspondence.
Perhaps, this system also can also comprise:
The average weight computing unit is used for the weight according to each user's label, calculates the average weight of each label in all users;
Long-tail label acquiring unit is used for this user's the label size according to the merchant of this user's relatively weight and described average weight is sorted, and will comes the label of the preset number of front, is defined as this user's long-tail label;
Described label recommends subelement specifically to be used for: carry out personalized recommendation according to described user's the long-tail label and the music sources tabulation of label correspondence.
More than to a kind of music sources personalized recommendation method provided by the present invention and system, be described in detail, used specific case herein principle of the present invention and embodiment are set forth, the explanation of above embodiment just is used for helping to understand method of the present invention and core concept thereof; Simultaneously, for one of ordinary skill in the art, according to thought of the present invention, part in specific embodiments and applications all can change.In sum, this description should not be construed as limitation of the present invention.
Claims (14)
1. a music sources personalized recommendation method is characterized in that, comprising:
Grasp the text message relevant from network with music sources;
Described text message is cut speech, add up the number of times that the label seed that occurs in the relevant textual information of each music sources and each label seed occur, and the number of times that occurs of the label seed that occurs in the relevant textual information of all music sources and each label seed;
The number of times that the label seed that occurs in the relevant textual information according to described all music sources and each label seed occur obtains the overall weight of each label seed;
For single music sources, number of times and overall weight thereof according to each label seed in its relevant textual information occurs obtain the monomer weight of each label seed with respect to single music sources;
The label seed that the monomer weight is met prerequisite is defined as the label of music sources;
Label according to described music sources carries out personalized recommendation.
2. method according to claim 1 is characterized in that, described label according to described music sources carries out personalized recommendation and comprises:
For music sources set, merge the label of each music sources in this set;
According to each label with respect to the monomer weight in each music sources, the weight of each label after obtaining merging;
The label that weight is met prerequisite is defined as the label that this music sources is gathered;
Label according to described music sources set carries out personalized recommendation.
3. method according to claim 2, it is characterized in that, described music sources set comprises each singer's music sources, and the label of described music sources set comprises each singer's label, and described label according to described music sources carries out personalized recommendation and comprises:
The weight of each singer's label is mapped to vector space, obtains each singer's label vector;
Cosine angle between the computation tag vector obtains each singer similarity between any two in twos;
Carry out personalized recommendation according to the similarity between the singer.
4. method according to claim 2, it is characterized in that, described music sources set comprises all music sources that each user listened, and the label of described music sources set comprises each user's label, and described label according to described music sources carries out personalized recommendation and comprises:
Label based on each music sources is set up inverted list, obtains the music sources tabulation of each label correspondence;
Carry out personalized recommendation according to user's the label and the music sources tabulation of label correspondence.
5. method according to claim 4 is characterized in that, also comprises:
According to each user's the behavior of listening to historical record, adjust each label weight of each user relatively.
6. according to claim 4 or 5 described methods, it is characterized in that, also comprise:
According to weight rank order from big to small, and will come the label of the preset number of front to user's label, be defined as this user's popular label;
The described label and the music sources tabulation of label correspondence according to the user carried out personalized recommendation and comprised: carry out personalized recommendation according to described user's the popular label and the music sources tabulation of label correspondence.
7. according to claim 4 or 5 described methods, it is characterized in that, also comprise:
According to the weight of each user's label, calculate the average weight of each label in all users;
This user's the label size according to the merchant of this user's relatively weight and described average weight is sorted, and will come the label of the preset number of front, be defined as this user's long-tail label;
The described label and the music sources tabulation of label correspondence according to the user carried out personalized recommendation and comprised: carry out personalized recommendation according to described user's the long-tail label and the music sources tabulation of label correspondence.
8. a music sources personalized recommendation system is characterized in that, comprising:
The information placement unit is used for grasping the text message relevant with music sources from network;
Statistic unit, be used for described text message is cut speech, add up the number of times that the label seed that occurs in the relevant textual information of each music sources and each label seed occur, and the number of times that occurs of the label seed that occurs in the relevant textual information of all music sources and each label seed;
Overall situation weight acquiring unit is used for the number of times that label seed that the relevant textual information according to described all music sources occurs and each label seed occur, and obtains the overall weight of each label seed;
Monomer weight acquiring unit is used for for single music sources, and number of times and overall weight thereof according to each label seed in its relevant textual information occurs obtain the monomer weight of each label seed with respect to single music sources;
The music label determining unit is used for the label seed that the monomer weight meets prerequisite is defined as the label of music sources;
Recommendation unit is used for carrying out personalized recommendation according to the label of described music sources.
9. system according to claim 8 is characterized in that, described recommendation unit comprises:
Label merges subelement, is used for merging the label of each music sources in this set for music sources set;
Weight merges subelement, is used for according to the monomer weight of each label with respect to each music sources, the weight of each label after obtaining merging;
The set label is determined subelement, is used for the label that weight meets prerequisite is defined as the label of this music sources set;
First recommends subelement, is used for carrying out personalized recommendation according to the label of described music sources set.
10. system according to claim 9 is characterized in that, described music sources set comprises each singer's music sources, and the label of described music sources set comprises each singer's label, and described first recommends subelement to comprise:
The label vector obtains subelement, is used for the weight of each singer's label is mapped to vector space, obtains each singer's label vector;
The similarity computation subunit is used for the cosine angle between the computation tag vector in twos, obtains each singer similarity between any two;
The singer recommends subelement, is used for carrying out personalized recommendation according to the similarity between the singer.
11. system according to claim 9 is characterized in that, described music sources set comprises all music sources that each user listened, and the label of described music sources set comprises each user's label, and described first recommends subelement to comprise:
Arrange subelement, be used for setting up inverted list, obtain the music sources tabulation of each label correspondence based on the label of each music sources;
Label is recommended subelement, is used for carrying out personalized recommendation according to user's the label and the music sources tabulation of label correspondence.
12. system according to claim 11 is characterized in that, also comprises:
Weight adjustment unit is used for the behavior of the listening to historical record according to each user, adjusts each label weight of each user relatively.
13. according to claim 11 or 12 described systems, it is characterized in that, also comprise:
Popular label acquiring unit is used for label to the user according to weight rank order from big to small, and will come the label of the preset number of front, is defined as this user's popular label;
Described label recommends subelement specifically to be used for: carry out personalized recommendation according to described user's the popular label and the music sources tabulation of label correspondence.
14. according to claim 11 or 12 described systems, it is characterized in that, also comprise:
The average weight computing unit is used for the weight according to each user's label, calculates the average weight of each label in all users;
Long-tail label acquiring unit is used for this user's the label size according to the merchant of this user's relatively weight and described average weight is sorted, and will comes the label of the preset number of front, is defined as this user's long-tail label;
Described label recommends subelement specifically to be used for: carry out personalized recommendation according to described user's the long-tail label and the music sources tabulation of label correspondence.
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